OpenRaiser/Intern-Atlas huggingface dataset:
A Mechanistic View on Video Generation as World Models: State and Dynamics
Venue: arXiv cs.CV 2026
Year: 2026

Large-scale video generation models have demonstrated emergent physical coherence, positioning them as potential world models. However, a gap remains between contemporary "stateless" video architectures and classic state-centric world model theories. This work bridges this gap by proposing a novel taxonomy centered on two pillars: State Construction and Dynamics Modeling. We categorize state construction into implicit paradigms (context management) and explicit paradigms (latent compression), while dynamics modeling is analyzed through knowledge integration and architectural reformulation. Furthermore, we advocate for a transition in evaluation from visual fidelity to functional benchmarks, testing physical persistence and causal reasoning. We conclude by identifying two critical frontiers: enhancing persistence via data-driven memory and compressed fidelity, and advancing causality through latent factor decoupling and reasoning-prior integration. By addressing these challenges, the field can evolve from generating visually plausible videos to building robust, general-purpose world simulators.
E2PL: Effective and Efficient Prompt Learning for Incomplete Multi-view Multi-Label Class Incremental Learning
Venue: arXiv cs.LG 2026
Year: 2026

Multi-view multi-label classification (MvMLC) is indispensable for modern web applications aggregating information from diverse sources. However, real-world web-scale settings are rife with missing views and continuously emerging classes, which pose significant obstacles to robust learning. Prevailing methods are ill-equipped for this reality, as they either lack adaptability to new classes or incur exponential parameter growth when handling all possible missing-view patterns, severely limiting their scalability in web environments. To systematically address this gap, we formally introduce a novel task, termed \emph{incomplete multi-view multi-label class incremental learning} (IMvMLCIL), which requires models to simultaneously address heterogeneous missing views and dynamic class expansion. To tackle this task, we propose \textsf{E2PL}, an Effective and Efficient Prompt Learning framework for IMvMLCIL. \textsf{E2PL} unifies two novel prompt designs: \emph{task-tailored prompts} for class-incremental adaptation and \emph{missing-aware prompts} for the flexible integration of arbitrary view-missing scenarios. To fundamentally address the exponential parameter explosion inherent in missing-aware prompts, we devise an \emph{efficient prototype tensorization} module, which leverages atomic tensor decomposition to elegantly reduce the prompt parameter complexity from exponential to linear w.r.t. the number of views. We further incorporate a \emph{dynamic contrastive learning} strategy explicitly model the complex dependencies among diverse missing-view patterns, thus enhancing the model's robustness. Extensive experiments on three benchmarks demonstrate that \textsf{E2PL} consistently outperforms state-of-the-art methods in both effectiveness and efficiency. The codes and datasets are available at https://anonymous.4open.science/r/code-for-E2PL.
A Characterization of Geodetic Graphs in Terms of their Embedded Even Graphs
Venue: arXiv cs.DM 2026
Year: 2026

The problem of finding the general classification of geodetic graphs is still open. We believe that one of the obstacles to attain this goal is that geodetic graphs lack a structural description. In other words, their fundamental properties have not yet been established in terms of the description of the complete graphs, paths and cycles contained within them. The absence of this information makes their generation and enumeration (as inherent parts of their general classification) a difficult task. This paper examines the structural qualities of geodetic graphs using their so-called embedded even graphs. To this effect, the necessary and sufficient conditions for eliminating the nongeodecity of each pair of C-opposite vertices in an even cycle C have been formulated, while the bigeodecity of the embedded even graphs of a geodetic graph has been established. In a sense, this allows us to arrive at the conclusion that the basic building blocks of geodetic graphs are precisely this class of bigeodetic ones.
Sink or SWIM: Tackling Real-Time ASR at Scale
Venue: arXiv cs.SD 2026
Year: 2026

Real-time automatic speech recognition systems are increasingly integrated into interactive applications, from voice assistants to live transcription services. However, scaling these systems to support multiple concurrent clients while maintaining low latency and high accuracy remains a major challenge. In this work, we present SWIM, a novel real-time ASR system built on top of OpenAI's Whisper model that enables true model-level parallelization for scalable, multilingual transcription. SWIM supports multiple concurrent audio streams without modifying the underlying model. It introduces a buffer merging strategy that maintains transcription fidelity while ensuring efficient resource usage. We evaluate SWIM in multi-client settings -- scaling up to 20 concurrent users -- and show that it delivers accurate real-time transcriptions in English, Italian, and Spanish, while maintaining low latency and high throughput. While Whisper-Streaming achieves a word error rate of approximately 8.2% with an average delay of approximately 3.4 s in a single-client, English-only setting, SWIM extends this capability to multilingual, multi-client environments. It maintains comparable accuracy with significantly lower delay -- around 2.4 s with 5 clients -- and continues to scale effectively up to 20 concurrent clients without degrading transcription quality and increasing overall throughput. Our approach advances scalable ASR by improving robustness and efficiency in dynamic, multi-user environments.
Low-Rank Tensor Approximation of Weights in Large Language Models via Cosine Lanczos Bidiagonalization
Venue: arXiv cs.LG 2026
Year: 2026

Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse natural language tasks but suffer from extremely large memory footprints and computational costs. In this paper, we introduce a tensor compression framework based on the cproduct for computing low rank approximation In the first part of our approach, we leverage the algebraic structure of the cproduct to represent weight tensors such as those in embedding layers, attention projections, and feed forward networks in a transform domain where frontal slices can be jointly approximated by low rank tensor factors. This enables computationally efficient compression that exploits multidimensional correlations beyond traditional SVD methods.
From Emotion to Expression: Theoretical Foundations and Resources for Fear Speech
Venue: arXiv cs.CL 2026
Year: 2026

Few forces rival fear in their ability to mobilize societies, distort communication, and reshape collective behavior. In computational linguistics, fear is primarily studied as an emotion, but not as a distinct form of speech. Fear speech content is widespread and growing, and often outperforms hate-speech content in reach and engagement because it appears "civiler" and evades moderation. Yet the computational study of fear speech remains fragmented and under-resourced. This can be understood by recognizing that fear speech is a phenomenon shaped by contributions from multiple disciplines. In this paper, we bridge cross-disciplinary perspectives by comparing theories of fear from Psychology, Political science, Communication science, and Linguistics. Building on this, we review existing definitions. We follow up with a survey of datasets from related research areas and propose a taxonomy that consolidates different dimensions of fear for studying fear speech. By reviewing current datasets and defining core concepts, our work offers both theoretical and practical guidance for creating datasets and advancing fear speech research.
Dynamic Role Assignment for Multi-Agent Debate
Venue: arXiv cs.CL 2026
Year: 2026

Multi-agent large language model (LLM) and vision-language model (VLM) debate systems employ specialized roles for complex problem-solving, yet model specializations are not leveraged to decide which model should fill which role. We propose dynamic role assignment, a framework that runs a Meta-Debate to select suitable agents before the actual debate. The meta-debate has two stages: (1) proposal, where candidates provide role-tailored arguments, and (2) peer review, where proposals are scored with data and role-specific criteria to choose the best agent for each position. We evaluate our method on LLM problem solving benchmarks. Applied on top of existing debate systems, our approach consistently outperforms uniform assignments (filling all roles with the same model) by up to 74.8% and random assignments (assigning models to roles without considering their suitability) by up to 29.7%, depending on the task and the specific assignment. This work establishes a new paradigm for multi-agent system design, shifting from static agent deployment to dynamic and capability-aware selection.
TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion
Venue: arXiv cs.CR 2026
Year: 2026

Hardware Trojans (HTs) remain a critical threat because learning-based detectors often overfit to narrow trigger/payload patterns and small, stylized benchmarks. We introduce TrojanGYM, an agentic, LLM-driven framework that automatically curates HT insertions to expose detector blind spots while preserving design correctness. Given high-level HT specifications, a suite of cooperating LLM agents (instantiated with GPT-4, LLaMA-3.3-70B, and Gemini-2.5Pro) proposes and refines RTL modifications that realize diverse triggers and payloads without impacting normal functionality. TrojanGYM implements a feedback-driven benchmark generation loop co-designed with HT detectors, in which constraint-aware syntactic checking and GNN-based HT detectors provide feedback that iteratively refines HT specifications and insertion strategies to better surface detector blind spots. We further propose Robust-GNN4TJ, a new implementation of the GNN4TJ with improved graph extraction, training robustness, and prediction reliability, especially on LLM-generated HT designs. On the most challenging TrojanGYM-generated benchmarks, Robust-GNN4TJ raises HT detection rates from 0% to 60% relative to a prior GNN-based detector. We instantiate TrojanGYM on SRAM, AES-128, and UART designs at RTL level, and show that it systematically produces diverse, functionally correct HTs that reach up to 83.33% evasion rates against modern GNN-based detectors, revealing robustness gaps that are not apparent when these detectors are evaluated solely on existing TrustHub-style benchmarks. Post peer-review, we will release all codes and artifacts.
Systematicity between Forms and Meanings across Languages Supports Efficient Communication
Venue: arXiv cs.CL 2026
Year: 2026

Languages vary widely in how meanings map to word forms. These mappings have been found to support efficient communication; however, this theory does not account for systematic relations within word forms. We examine how a restricted set of grammatical meanings (e.g. person, number) are expressed on verbs and pronouns across typologically diverse languages. Consistent with prior work, we find that verb and pronoun forms are shaped by competing communicative pressures for simplicity (minimizing the inventory of grammatical distinctions) and accuracy (enabling recovery of intended meanings). Crucially, our proposed model uses a novel measure of complexity (inverse of simplicity) based on the learnability of meaning-to-form mappings. This innovation captures fine-grained regularities in linguistic form, allowing better discrimination between attested and unattested systems, and establishes a new connection from efficient communication theory to systematicity in natural language.
High-Rate Quantized Matrix Multiplication: Theory and Practice
Venue: arXiv cs.IT 2026
Year: 2026

This work investigates the problem of quantized matrix multiplication (MatMul), which has become crucial for the efficient deployment of large language models (LLMs). We consider two settings: 1) Generic MatMul, where both matrices must be quantized (weight+activation quantization); and 2) weight-only quantization, where the second matrix is only known through covariance matrix $Σ_X$ of its columns. For each setting, we first review the fundamental information-theoretic tradeoff between quantization rate and distortion (high-rate theory), and then analyze the performance of several popular quantization schemes, comparing them to these fundamental limits. Specifically, we discuss rate loss (compared to information theoretic optima) of absmax INT and floating-point (FP) quantization, for which we also derive remarkably accurate heuristic approximations. Weight-only quantization is related to the problem of weighted mean squared error (WMSE) source coding, whose classical (reverse) waterfilling solution dictates how one should distribute rate between coordinates of the vector. We show how waterfilling can be used to improve practical LLM quantization algorithms (GPTQ), which at present allocate rate equally. This new scheme (termed ``WaterSIC'') only uses scalar INT quantizers, but its high-rate performance is basis free (it depends only on the determinant of $Σ_X$ and, thus, unlike existing schemes, is immune to applying random rotations) and is within a multiplicative factor of $\frac{2πe}{12}$ (or 0.25 bit/entry) of the information-theoretic distortion limit (!). GPTQ's performance is affected by the choice of basis, but for a random rotation and actual $Σ_X$ from Llama-3-8B we find GPTQ to be within 0.1 bit (depending on the layer type) of WaterSIC, suggesting that GPTQ with random rotation is also near optimal (for high-rate quantization).
PUNCH: Physics-informed Uncertainty-aware Network for Coronary Hemodynamics
Venue: arXiv cs.LG 2026
Year: 2026

More than 10 million coronary angiograms are performed globally each year, providing a gold standard for detecting obstructive coronary artery disease. Yet, no obstructive lesions are identified in 70% of patients evaluated for ischemic heart disease. Up to half of these patients have undiagnosed, life-limiting coronary microvascular dysfunction (CMD), which remains under-detected due to the limited availability of invasive tools required to measure coronary flow reserve (CFR). Here, we introduce PUNCH, a non-invasive, uncertainty-aware framework for estimating CFR directly from standard coronary angiography. PUNCH integrates physics-informed neural networks with variational inference to infer coronary blood flow from first-principles models of contrast transport, without requiring ground-truth flow measurements or population-level training. The pipeline runs in approximately three minutes per patient on a single GPU. Validated on synthetic angiograms with controlled noise and imaging artifacts, as well as on clinical bolus thermodilution data from 20 patients, PUNCH demonstrates accurate and uncertainty-calibrated CFR estimation. This approach establishes a new paradigm for CMD diagnosis and illustrates how physics-informed inference can substantially expand the diagnostic utility of available clinical imaging.
JetFormer: A Scalable and Efficient Transformer for Jet Tagging from Offline Analysis to FPGA Triggers
Venue: arXiv cs.LG 2026
Year: 2026

We present JetFormer, a versatile and scalable encoder-only Transformer architecture for particle jet tagging at the Large Hadron Collider (LHC). Unlike prior approaches that are often tailored to specific deployment regimes, JetFormer is designed to operate effectively across the full spectrum of jet tagging scenarios, from high-accuracy offline analysis to ultra-low-latency online triggering. The model processes variable-length sets of particle features without relying on input of explicit pairwise interactions, yet achieves competitive or superior performance compared to state-of-the-art methods. On the large-scale JetClass dataset, a large-scale JetFormer matches the accuracy of the interaction-rich ParT model (within 0.7%) while using 37.4% fewer FLOPs, demonstrating its computational efficiency and strong generalization. On benchmark HLS4ML 150P datasets, JetFormer consistently outperforms existing models such as MLPs, Deep Sets, and Interaction Networks by 3-4% in accuracy. To bridge the gap to hardware deployment, we further introduce a hardware-aware optimization pipeline based on multi-objective hyperparameter search, yielding compact variants like JetFormer-tiny suitable for FPGA-based trigger systems with sub-microsecond latency requirements. Through structured pruning and quantization, we show that JetFormer can be aggressively compressed with minimal accuracy loss. By unifying high-performance modeling and deployability within a single architectural framework, JetFormer provides a practical pathway for deploying Transformer-based jet taggers in both offline and online environments at the LHC. Code is available at https://github.com/walkieq/JetFormer.
Studying Mobile Spatial Collaboration across Video Calls and Augmented Reality
Venue: arXiv cs.HC 2026
Year: 2026

Mobile video calls are widely used to share information about real-world objects and environments with remote collaborators. While these calls provide valuable visual context in real time, the experience of interacting with people and moving around a space is significantly reduced when compared to co-located conversations. Recent work has demonstrated the potential of Mobile Augmented Reality applications to enable more spatial forms of collaboration across distance. To better understand the dynamics of mobile AR collaboration and how this medium compares against the status quo, we conducted a comparative structured observation study to analyze people's perception of space and interaction with remote collaborators across mobile video calls and AR-based calls. Fourteen pairs of participants completed a spatial collaboration task using each medium. Through a mixed-methods analysis of session videos, transcripts, motion logs, post-task exercises, and interviews, we highlight how the choice of medium influences the roles and responsibilities that collaborators take on and the construction of a shared language for coordination. We discuss the importance of spatial reasoning with one's body, how video calls help participants "be on the same page" more directly, and how AR calls enable both onsite and remote collaborators to engage with the space and each other in ways that resemble in-person interaction. Our study offers a nuanced view of the benefits and limitations of both mediums, and we conclude with a discussion of design implications for future systems that integrate mobile video and AR to better support spatial collaboration in its many forms.
Constant-time Connectivity and 2-Edge Connectivity Querying in Dynamic Graphs
Venue: arXiv cs.DB 2026
Year: 2026

Connectivity query processing is a fundamental problem in graph processing. Given an undirected graph and two query vertices, the problem aims to identify whether they are connected via a path. Given frequent edge updates in real graph applications, in this paper, we study connectivity query processing in fully dynamic graphs, where edges are frequently inserted or deleted. A recent solution, called D-tree, maintains a spanning tree for each connected component and applies several heuristics to reduce the depth of the tree. To improve efficiency, we propose a new spanning-tree-based solution by maintaining a disjoint-set tree simultaneously. By combining the advantages of two trees, we achieve the constant query time complexity and also significantly improve the theoretical running time in both edge insertion and edge deletion. In addition, we extend our connectivity maintenance algorithms to maintain 2-edge connectivity. Our performance studies on real large datasets show considerable improvement of our algorithms.
Real-Time Synchronized Interaction Framework for Emotion-Aware Humanoid Robots
Venue: arXiv cs.RO 2026
Year: 2026

As humanoid robots increasingly introduced into social scene, achieving emotionally synchronized multimodal interaction remains a significant challenges. To facilitate the further adoption and integration of humanoid robots into service roles, we present a real-time framework for NAO robots that synchronizes speech prosody with full-body gestures through three key innovations: (1) A dual-channel emotion engine where large language model (LLM) simultaneously generates context-aware text responses and biomechanically feasible motion descriptors, constrained by a structured joint movement library; (2) Duration-aware dynamic time warping for precise temporal alignment of speech output and kinematic motion keyframes; (3) Closed-loop feasibility verification ensuring gestures adhere to NAO's physical joint limits through real-time adaptation. Evaluations show 21% higher emotional alignment compared to rule-based systems, achieved by coordinating vocal pitch (arousal-driven) with upper-limb kinematics while maintaining lower-body stability. By enabling seamless sensorimotor coordination, this framework advances the deployment of context-aware social robots in dynamic applications such as personalized healthcare, interactive education, and responsive customer service platforms.
Dynamic Meta-Ensemble Framework for Efficient and Accurate Deep Learning in Plant Leaf Disease Detection on Resource-Constrained Edge Devices
Venue: arXiv cs.CV 2026
Year: 2026

Deploying deep learning models for plant disease detection on edge devices such as IoT sensors, smartphones, and embedded systems is severely constrained by limited computational resources and energy budgets. To address this challenge, we introduce a novel Dynamic Meta-Ensemble Framework (DMEF) for high-accuracy plant disease diagnosis under resource constraints. DMEF employs an adaptive weighting mechanism that dynamically combines the predictions of three lightweight convolutional neural networks (MobileNetV2, NASNetMobile, and InceptionV3) by optimizing a trade-off between accuracy improvements (DeltaAcc) and computational efficiency (model size). During training, the ensemble weights are updated iteratively, favoring models exhibiting high performance and low complexity. Extensive experiments on benchmark datasets for potato and maize diseases demonstrate state-of-the-art classification accuracies of 99.53% and 96.61%, respectively, surpassing standalone models and static ensembles by 2.1% and 6.3%. With computationally efficient inference latency (<75ms) and a compact footprint (<1 million parameters), DMEF shows strong potential for edge-based agricultural monitoring, suggesting viability for scalable crop disease management. This bridges the gap between high-accuracy AI and practical field applications.